Dynamic network models for forecasting
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Principles of mixed-initiative user interfaces
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Conversation as Action Under Uncertainty
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
A model for projection and action
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Display of information for time-critical decision making
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Stochastic simulation algorithms for dynamic probabilistic networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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Speaker-independent speech recognition systems are being used with increasing frequency for command and control applications. To date, users of such systems must contend with their fragility to subtle changes in language usage and environmental acoustics. We describe work on coupling speech recognition systems with temporal probabilistic user models that provide inferences about the intentions associated with utterances. The methods can be employed to enhance the robustness of speech recognition by endowing systems with an ability to reason about the costs and benefits of action in a setting and to make decisions about the best action to take given uncertainty about the meaning behind acoustic signals. The methods have been implemented in the form of a dialog clarification module that can be integrated with legacy spoken language systems. We describe representation and inference procedures and present details on the operation of an implemented spoken command and control development environment called DeepListener.